General Index Design Guidelines

Experienced database administrators can design a good set of indexes, but this task is very complex, time-consuming, and error-prone even for moderately complex databases and workloads. Understanding the characteristics of your database, queries, and data columns can help you design optimal indexes.

Large numbers of indexes on a table affect the performance of INSERT, UPDATE, DELETE, and MERGE statements because all indexes must be adjusted appropriately as data in the table changes.

Avoid over-indexing heavily updated tables and keep indexes narrow, that is, with as few columns as possible.

Use many indexes to improve query performance on tables with low update requirements, but large volumes of data. Large numbers of indexes can help the performance of queries that do not modify data, such as SELECT statements, because the query optimizer has more indexes to choose from to determine the fastest access method.

Indexing small tables may not be optimal because it can take the query optimizer longer to traverse the index searching for data than to perform a simple table scan. Therefore, indexes on small tables might never be used, but must still be maintained as data in the table changes.

Indexes on views can provide significant performance gains when the view contains aggregations, table joins, or a combination of aggregations and joins. The view does not have to be explicitly referenced in the query for the query optimizer to use it. For more information, see Designing Indexed Views.

Covering indexes can improve query performance because all the data needed to meet the requirements of the query exists within the index itself. That is, only the index pages, and not the data pages of the table or clustered index, are required to retrieve the requested data; therefore, reducing overall disk I/O. For example, a query of columns a and b on a table that has a composite index created on columns a, b, and c can retrieve the specified data from the index alone.

Write queries that insert or modify as many rows as possible in a single statement, instead of using multiple queries to update the same rows. By using only one statement, optimized index maintenance could be exploited.

Evaluate the query type and how columns are used in the query. For example, a column used in an exact-match query type would be a good candidate for a nonclustered or clustered index. For more information, see Query Types and Indexes.

Keep the length of the index key short for clustered indexes. Additionally, clustered indexes benefit from being created on unique or nonnull columns. For more information, see Clustered Index Design Guidelines.

Columns that are of the ntext, text, image, varchar(max), nvarchar(max), and varbinary(max) data types cannot be specified as index key columns. However, varchar(max), nvarchar(max), varbinary(max), and xml data types can participate in a nonclustered index as nonkey index columns. For more information, see Index with Included Columns.

Examine column uniqueness. A unique index instead of a nonunique index on the same combination of columns provides additional information for the query optimizer that makes the index more useful. For more information, see Unique Index Design Guidelines.

Examine data distribution in the column. Frequently, a long-running query is caused by indexing a column with few unique values, or by performing a join on such a column. This is a fundamental problem with the data and query, and generally cannot be resolved without identifying this situation. For example, a physical telephone directory sorted alphabetically on last name will not expedite locating a person if all people in the city are named Smith or Jones. For more information about data distribution, see Using Statistics to Improve Query Performance.

Consider using filtered indexes on columns that have well-defined subsets, for example sparse columns, columns with mostly NULL values, columns with categories of values, and columns with distinct ranges of values. A well-designed filtered index can improve query performance, reduce index maintenance costs, and reduce storage costs. For more information, see Filtered Index Design Guidelines.

Consider the order of the columns if the index will contain multiple columns. The column that is used in the WHERE clause in an equal to (=), greater than (>), less than (<), or BETWEEN search condition, or participates in a join, should be placed first. Additional columns should be ordered based on their level of distinctness, that is, from the most distinct to the least distinct.

For example, if the index is defined as LastName, FirstName the index will be useful when the search criterion is WHERE LastName = 'Smith' or WHERE LastName = Smith AND FirstName LIKE 'J%'. However, the query optimizer would not use the index for a query that searched only on FirstName (WHERE FirstName = 'Jane').

After you have determined that an index is appropriate for a query, you can select the type of index that best fits your situation. Index characteristics include the following:

Clustered versus nonclustered

Unique versus nonunique

Single column versus multicolumn

Ascending or descending order on the columns in the index

Full-table versus filtered for nonclustered indexes

You can also customize the initial storage characteristics of the index to optimize its performance or maintenance by setting an option such as FILLFACTOR. For more information, see Setting Index Options. Also, you can determine the index storage location by using filegroups or partition schemes to optimize performance. For more information, see Placing Indexes on Filegroups.